Artificially intelligent irrigation system

ABSTRACT

An artificially intelligent irrigation system on a property may include an irrigation management server with the information for the irrigation system. An artificial intelligence feature may retrieve and access inputs from a plurality of resources or data sources. These sources may include current weather data, historical weather data, current moisture levels, historical moisture levels, sensor information from sensors on or near the property, water utility usage data, and other data. Other inputs may be events on the property as well the frequently or consistently occur and may also be considered historical data. The artificial intelligence feature may manage the schedule and predict the upcoming water schedule based on this information and appropriately water, or not water, or change duration of watering or output of watering based on the information gathered without human intervention.

This application is a continuation of U.S. patent application Ser. No.16/854,907, filed May 27, 2020, which claims priority to U.S.Provisional Application Ser. No. 62/859,517, filed Jun. 10, 2019, whichare hereby incorporated by reference in their entireties.

FIELD

This disclosure relates generally to an irrigation system that assumesactions of a landscape owner and even more specifically an irrigationsystem that learns from sources within the irrigation system and outsidethe irrigation system to artificially manage the irrigation systemwithout human interaction, or with minimized human interaction. Theirrigation system may adjust, manage and implement the system using aplurality of resources.

RELATED ART

Controllers for irrigation systems have been utilized for decades tomake it easier on property owners to water their landscapes. Simplesystems for watering may include a start time and finish time forspecific zones for a given landscape.

Smart controllers have become more and more common in the irrigationindustry. Smart controllers and smart controller technology allows for auser to quickly and effectively make changes to an irrigation system, orsystem.

Commercial and residential systems are taking advantage of smartcontrollers and the use of sensors to more effectively manage waterusage. Systems often have an easy way or method for a user to nowinteract with the irrigation systems by having an application (or app)on a phone that allows them to make changes to the system. Othercomputers or tablets may also be used to manipulate and control thesystem by relaying messages to the smart controllers which carry out thetasks.

Some smart controllers may even learn from the way people control them.For example, some smart thermostats have applied an individualrules-and-exceptions-based learning approach to automatically generatetemperature setpoint schedules. The rules-and-exceptions-based learningapproach may involve observing interactions with the smart thermostatover time and, based on certain defined rules and exceptions, determinewhether the interactions have some meaning that should be used to builda temperature setpoint schedule. However, rules-and-exceptions-basedlearning may not be the most effective way to control an irrigationsystem, as users do not always set rules and schedules that reflect themost efficient watering schedule. For example, many users set theirirrigation systems to water at a specific time every day, and neglect tochange the schedule if precipitation occurs.

Additionally, there may be events other than precipitation that mayaffect soil moisture levels at the user's property after a lag time. Forexample, a watering event at a nearby property may affect soil moisturelevels at the user's property after a lag time. Precipitation events atremote locations may also, after a given lag time, affect soil moisturelevels at the user's property. Thus, setting a watering schedule towater only based on the current soil moisture level may not be the mostefficient watering schedule. However, it is difficult to train a smartcontroller to water based on events that occur away from the user'sproperty because it may be difficult to establish a ground truth soilmoisture based on such events, needed to determine a soil moisturefunction that maps predictive soil moisture to the data from remotelocations.

Thus, there is a need for a smart watering system that can generate atraining data set to determine soil moisture functions that mappredictive soil moisture to the data from remote locations. It may alsobe advantageous for the system to continuously monitor ground truth soilmoisture to check the accuracy of the soil moisture function for thepredictive soil moisture, and update a baseline watering schedule as aresult of the predictive soil moisture.

SUMMARY

One aspect of the present disclosure is directed to an artificialintelligence system for predicting soil moisture and using thepredictive soil moisture to manage watering of an irrigation system.

This disclosure, in at least one aspect, relates to irrigation systems,including utility and function of an irrigation system with minimal orno human involvement. More generally the disclosure relates to a systemwith inputs from within an irrigation system and outside the irrigationsystem to manage the watering and irrigation of a given property. Theinputs within the irrigation system may include, for example, water flowmeasurements, soil moisture levels, etc. Inputs from outside theirrigation system may include, for example, third party weather servicedata for a location remote from the user's property, water flowmeasurements at a second property, and/or soil moisture levels at asecond property, etc.

The system may provide several different solutions to watering ofproperty or landscape. An artificial intelligence (AI) feature may beutilized to provide for a plurality of inputs to be made into the systemto have the system run primarily or entirely on its own. Firstly, oneportion of the method to allow the AI feature to function is the AI mayauto-review prior adjustments to watering zones, program changes orcreations based on historical data and adjusts schedules and times basedon this data alone to create a baseline watering schedule.

Secondly, the AI may use local sensor data from an extended network ofirrigation systems, including but not limited to moisture sensors, rainsensors, water flow sensors, and other water station sensors and gatherlocal data within a given range (such as a 5 to 15 mile radius, but therange may have other suitable distances, shapes and sizes depending onthe property). Based on this collected data by itself or in connectionwith the historical data, the AI may generate a training data set thatdetermines a soil moisture function that maps predictive soil moistureto the collected data. Based on the predictive soil moisture, the AI mayautomatically make changes to the baseline water programs and/or zones.

Thirdly, information based on historical micro climate data, such asrain fall or other weather data, from other sites, or landscape sites,and how it may impact surrounding sites may be collected. An example maybe a primary city receives 0.25 inches of rain then based on previoushistorical data for surrounding geographic locations historical data asuburb where the user's property is located may receive at least 0.20inches of rain, and with such date the AI reviews this data from otherregions and may make informed decisions based on the specific irrigationsite. This third mechanism may be utilized alone or in conjunction withthe first feature and second feature to provide input for the AI to makean informed decision and adjustment to the irrigation system.

Additional inputs are contemplated and expected such as local wateringlaws and ordinances as well as local utility information for waterusage. Each of these different inputs into the system allow the systemto self-manage with the AI feature providing better watering andappropriate water conservation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an irrigation system, in accordance withdisclosed embodiments.

FIG. 2 is a block diagram of an exemplary prediction system, inaccordance with disclosed embodiments.

FIG. 3 is a block diagram of an exemplary model generator, in accordancewith disclosed embodiments.

FIG. 4 is a block diagram of an exemplary extended irrigation network.

FIG. 5 is a block diagram of an exemplary client device.

FIG. 6 is an exemplary flow chart illustrating a prediction process, inaccordance with disclosed embodiments.

FIG. 7 is an exemplary flow chart illustrating a training datageneration process, in accordance with disclosed embodiments.

FIG. 8A is an exemplary soil moisture function mapping ground truth soilmoisture to a triggering event in accordance with disclosed embodiments.

FIG. 8B is another exemplary soil moisture function mapping ground truthsoil moisture to a triggering event in accordance with disclosedembodiments.

FIG. 8C is another exemplary soil moisture function mapping ground truthsoil moisture to a triggering event in accordance with disclosedembodiments.

FIG. 9 is an exemplary flow chart illustrating an irrigation controlsystem process.

DETAILED DESCRIPTION

Before the present invention is disclosed and described in detail, itshould be understood that the present disclosure is not limited to anyparticular structures, process steps, or materials discussed ordisclosed herein, but is extended to include equivalents thereof aswould be recognized by those of ordinary skill in the relevant art. Morespecifically, the invention is defined by the terms set forth in theclaims. The discussion of any particular aspect of the invention is notto be understood as a requirement that such aspect must be present apartfrom an express inclusion of the aspect in the claims. As used in thisspecification and the appended claims, singular forms such as “a,” “an,”and “the” may include the plural unless the context clearly dictatesotherwise. Thus, for example, reference to “a watering instruction” mayinclude one or more of such watering instructions, and reference to “themoisture sensor” may include reference to one or more of such moisturesensors.

In the present document, the word “exemplary” is used herein to mean“serving as an example, instance, or illustration.” Any embodiment orimplementation of the present subject matter described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. It will also be appreciated bythose skilled in the art that the words during, while, and when as usedherein are not exact terms that mean an action takes place instantlyupon an initiating action but that there may be some small butreasonable delay, such as a propagation delay, between the initialaction and the reaction that is initiated by the initial action.Additionally, the word “connected” and “coupled” is used throughout forclarity of the description and can include either a direct connection oran indirect connection.

The following description sets forth a system, for managing irrigationand watering either residentially, commercially or agriculturally. Thesystem may include multiple inputs from a plurality of sources toappropriately provide enough information to the system so that the idealamount of moisture to a landscape is administered.

The disclosure is also directed to a machine learning artificialintelligence system for predicting soil moisture levels at a propertyand communicating them to an irrigation system to execute wateringinstructions at the property based on the predictive soil moisturelevels. The artificial intelligence system may model a property's soilmoisture levels using various data, such as third party weather data ata remote location, system weather data at a remote location, soilmoisture levels at a remote location, and water flow data at a remotelocation or from a local utility company. As used herein, “triggeringevent” comprises any event that the system determines could lead to achange in a property's soil moisture levels and includes, withoutlimitation, weather service data for a location remote from theproperty, third party weather service data, and data taken from anextended network of irrigation systems, such as weather data at a remoteproperty in the extended network, soil moisture levels at a remoteproperty in the extended network, and/or water flow data at a remoteproperty in the extended network. As used herein, “remote” means aproperty other than the specific user's property. A remote property maybe adjacent to the user's property, and may also include, for example,properties within a specific radius, such as a 5-mile radius, 10-mileradius, 20-mile radius, etc. Remote properties may also be referred toherein as “a second property” and the user's property may be referred toas “a first property,” “user's property,” and/or “client's property.”

In some embodiments, the system may continuously or nearly continuouslyrequest a prediction for soil moisture content at the property. Theprediction system may analyze data from remote properties or locations,such as soil moisture data at a remote location, using predictionmodels. The prediction models may be generated using machine learningalgorithms that study training data sets that associate the datareceived with corresponding “ground truth,” that is, informationprovided by direct measurement of soil moisture levels by soil moisturesensors at the property as opposed to information provided byprediction. The machine learning algorithms may update and tailor theprediction models based on the ground truth. The prediction system mayoutput a prediction of soil moisture levels at the property based on theanalysis of data from remote locations, such as soil moisture data at aremote location. The prediction system may, for example, determine asoil moisture function that maps predictive soil moisture to the datareceived. The prediction system may then store the soil moisturefunction, along with metadata associated with the triggering event (suchas a location of the triggering event, time of day, date, airtemperature, length/volume/rate of triggering event, etc.). The storedsoil moisture functions may be retrieved at a later time to make afuture soil moisture level prediction.

In some embodiments, the prediction system may be coupled with databasesthat store historical weather data and use data processing methods tocurate information and facilitate data analysis. In other embodiments,the prediction system may improve accuracy by using iterative methods inwhich multiple prediction models are generated and then aggregated. Inyet other embodiments, the prediction system may include hardwareconfigured to efficiently conduct filtering, sorting, and parallelcalculation tasks to improve computing time and cost.

FIG. 1 shows illustrates an exemplary implementation 100 of anartificially intelligent irrigation system, implemented in a network106, according to an embodiment of the present disclosure. Systems andmethods are described herein for providing a method and system for usingartificial intelligence to predict soil moisture levels, and in turnusing the predictive moisture levels to control an irrigation system,including an irrigation controller 102 in communication with hardware108 at the user's property, such as sprinklers 112. The system 100 maybe used to predict soil moisture content at a specific property havingan irrigation system, and then may be used to direct hardware, such assprinklers 112, to either provide more or less water to the propertybased on the prediction. The system may include a prediction system 204,a model generator 304, and one or more of the following connected via anetwork 106: client devices 104, databases 114, weather service data146, hardware 108 at the first property, and hardware 108′ at one ormore remote properties. In some embodiments, as shown in FIG. 1 , eachcomponent of system 100 may be connected to a network 106. However, inother embodiments components of system 100 may be connected directlywith each other, without network 106. Additional components may be partof the system, and the prediction system may take into considerationdata from additional inputs, such as data from a local water utility,etc.

The network 106 may be a wireless or a wired network, or a combinationthereof. The network 106 can be a collection of individual networks,interconnected with each other and functioning as a single large network(e.g., the interne or an intranet). Examples of such individual networksinclude, but are not limited to, Global System for Mobile Communication(GSM) network, Universal Mobile Telecommunications System (UMTS)network, Personal Communications Service (PCS) network, Time DivisionMultiple Access (TDMA) network, Code Division Multiple Access (CDMA)network, Next Generation Network (NGN), Public Switched TelephoneNetwork (PSTN), and Integrated Services Digital Network (ISDN).Depending on the technology, the network 106 includes various networkentities, such as gateways, routers; however, such details have beenomitted for ease of understanding.

Weather service data 146 may be a system associated with a third partyweather service, such as an entity that provides weather data. Weatherservice data 146 may store information about weather that includes, forexample, precipitation data, and specific data associated with theprecipitation data, such as the time of day of precipitation, thelocation and amount of precipitation, the date of precipitation, thetemperature at the time of precipitation, and/or other weather dataknown to those skilled in the art.

Client devices 104 may include software that when executed by aprocessor performs known Internet-related communication and contentdisplay processes. For instance, client devices 104 may execute browsersoftware that generates and displays interfaces including content on adisplay device included in, or connected to, client devices 104. Clientdevices 104 may execute applications that allows client devices 104 tocommunicate with components over network 106, and generate and displaycontent in interfaces via display devices included in client devices104. The disclosed embodiments are not limited to any particularconfiguration of client devices 104. For instance, a client device 104may be a mobile device that stores and executes mobile applications thatprovide functions offered by prediction system 204 and/or hardware 108,such as moisture sensors.

Databases 114 may include one or more computing devices configured withappropriate software to perform operations consistent with providingprediction system 204 and model generator 304 with data associated withhistorical weather data and other triggering events. Databases 114 mayinclude, for example, Oracle™ databases, Sybase™ databases, or otherrelational databases or non-relational databases, such as Hadoop™sequence files, HBase™, or Cassandra™ Database(s) 180 may includecomputing components (e.g., database management system, database server,etc.) configured to receive and process requests for data stored inmemory devices of the database(s) and to provide data from thedatabase(s).

Data associated with weather may include, for example, historicalweather data identifying precipitation and associated data there with,such as location of the precipitation, data identifying the measuredamount of precipitation over time, data describing the date and time ofthe precipitation, data indicating the air temperature at time ofprecipitation, etc. While databases 114 are shown separately, in someembodiments databases 114 may be included in or otherwise related to oneor more of prediction system 204 and model generator 304.

Databases 114 may be established on a local server, a remote server, onthe cloud, etc., and may be configured to collect and/or maintain thedata from the weather, the remote moisture flow sensors, and/or theremote soil moisture sensors and provide it to the prediction system204, model generator 304, and client devices 104. Databases 114 maycollect the data from a variety of sources, including, for instance,historical weather data, third party weather data, model generator 304,local water utilities, and/or third-party systems (not shown). Othersources of data associated with weather are possible as well.

Prediction system 204 may include one or more computing systemsconfigured to perform one or more operations consistent with modelingthe soil moisture levels of a specific property. Prediction system 204may generate a prediction result based on the information received fromone or more of, for example, weather service data 146, online resources,hardware 108, remote hardware 108′, database 114, etc. Data relevant tothe prediction system may include real-time water flow data, real-timeprecipitation data, real-time air temperature data, real-time soiltemperature data, and/or real-time soil moisture level data. Predictionsystem 204 may determine a soil moisture function that maps predictivesoil moisture to the data received. Prediction system 204 is furtherdescribed below in connection with FIG. 2 .

Model generator 304 may include one or more computing systems configuredto generate prediction models to estimate soil moisture at a specificproperty using the weather and/or watering data in relation to groundtruth soil moisture levels. Model generator 304 may receive or obtaininformation from databases 114, hardware 108′, hardware 108, and/orweather data service 146.

In some embodiments, model generator 304 may receive a specific requestfrom prediction system 204. In other configurations, model generatordoes not receive a specific request from prediction system, but ratherbegins generating prediction models automatically as soon as the modelgenerator is turned “on” by a client. Prediction models may includestatistical algorithms that are used to determine the probability of anoutcome, given a set amount of input data. For example, predictionmodels may include regression models that estimate the relationshipsamong input and output variables. Prediction models may also sortelements of a dataset using one or more classifiers to determine theprobability of a specific outcome. Prediction models may be parametric,non-parametric, and/or semi-parametric models.

In some embodiments, prediction models may cluster points of data infunctional groups such as “random forests.” Random Forests may comprisecombinations of decision tree predictors. (Decision trees may comprise adata structure mapping observations about something, in the “branch” ofthe tree, to conclusions about that thing's target value, in the“leaves” of the tree.) Each tree may depend on the values of a randomvector sampled independently and with the same distribution for alltrees in the forest. Prediction models may also include artificialneural networks. Artificial neural networks may model input/outputrelationships of variables and parameters by generating a number ofinterconnected nodes which contain an activation function. Theactivation function of a node may define a resulting output of that nodegiven an argument or a set of arguments. Artificial neural networks maygenerate patterns to the network via an “input layer” which communicatesto one or more “hidden layers” where the system determines regressionsvia a weighted connections. Prediction models may additionally oralternatively include classification and regression trees, or othertypes of models known to those skilled in the art. Each of theseprediction models may be used alone or in combination with others. Modelgenerator 304 may submit models to predict soil moisture levels. Togenerate prediction models, model generator 304 may analyze informationapplying machine-learning methods. Model generator 304 may communicateback with prediction system 204 via network 106 or other communicationavenues. Model generator 304 is further described below in connectionwith FIG. 3 .

FIG. 1 shows prediction system 204 and model generator 304 as adifferent components. However, prediction system 204 and model generator304 may be implemented in the same computing system. For example,prediction system 204 and model generator 304 may be embodied in asingle server. Network 106 may be any type of network configured toprovide communications between components of system 100. For example,network 106 may be any type of network (including infrastructure) thatprovides communications, exchanges information, and/or facilitates theexchange of information, such as the Internet, a Local Area Network,near field communication (NFC), optical code scanner, or other suitableconnection(s) that enables the sending and receiving of informationbetween the components of system 100. In other embodiments, one or morecomponents of system 100 may communicate directly through a dedicatedcommunication link(s).

It is to be understood that the configuration and boundaries of thefunctional building blocks of system 100 have been defined herein forthe convenience of the description. Alternative boundaries can bedefined so long as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments.

FIG. 2 shows a block diagram of an exemplary prediction system 204, inaccordance with disclosed embodiments. Prediction system 204 may includea communication device 214, a prediction memory 220, and one or moreprediction processors 208. Prediction memory 220 may include predictionprograms 236, prediction data 234, and other data 244. Predictionprocessor 208 may include triggering event data selector 226, dataaggregator 228, and prediction engine 230.

In some embodiments, prediction system 204 may take the form of aserver, general purpose computer, mainframe computer, or any combinationof these components. Other implementations consistent with disclosedembodiments are possible as well. Communication device 214 may beconfigured to communicate with one or more databases 114, weatherservice data 146, hardware 108′ hardware 108, etc. In particular,communication device 214 may be configured to receive inputs fromhardware at remote properties data associated with triggering events. Inaddition, communication device 214 may be configured to communicate withother components as well, including, for example, a model generator 304.Communication device 214 may include, for example, one or more digitaland/or analog devices that allow communication device 214 to communicatewith and/or detect other components, such as a network controller and/orwireless adaptor for communicating over the Internet. Otherimplementations consistent with disclosed embodiments are also possible.

Prediction memory 220 may include one or more storage devices configuredto store instructions used by soil moisture prediction processor 208 toperform functions related to disclosed embodiments. For example,prediction memory 220 may store software instructions, such asprediction program 236, which may perform one or more operations whenexecuted by prediction processor 208. The disclosed embodiments are notlimited to separate programs or computers configured to performdedicated tasks. For example, prediction memory 220 may include a singleprediction program 236 that performs the functions of prediction system204, or prediction program 236 may comprise multiple programs.Prediction memory 220 may also store prediction data 234 that is used byprediction program(s) 236.

In certain configurations, prediction memory 220 may store sets ofinstructions for carrying out processes to model soil moisture levels ata specific property, generate a soil moisture prediction schedule,and/or generate a soil moisture function that maps predictive soilmoisture to the data from the triggering event, described in connectionwith FIGS. 6-9 . In general, instructions may be executed by predictionprocessor 208 to perform one or more processes consistent with disclosedembodiments. In some embodiments, soil moisture prediction processor 208may include one or more known processing devices, such as, but notlimited to, microprocessors from the Pentium™ or Xeon™ familymanufactured by Intel™, the Turion™ family manufactured by AMD™, or anyof various processors from other manufacturers. However, in otherembodiments, soil moisture prediction processor 208 may be a pluralityof devices coupled and configured to perform functions in accordancewith the disclosure. The functions of the various elements shown in thefigure, including any functional blocks labeled as “processor(s)”, maybe provided through the use of dedicated hardware as well as hardwarecapable of executing software in association with appropriate software.When provided by a processor, the functions may be provided by a singlededicated processor, by a single shared processor, or by a plurality ofindividual processors, some of which may be shared. Moreover, explicituse of the term “processor” should not be construed to refer exclusivelyto hardware capable of executing software, and may implicitly include,without limitation, digital signal processor (DSP) hardware, networkprocessor, application specific integrated circuit (ASIC), fieldprogrammable gate array (FPGA), read only memory (ROM) for storingsoftware, random access memory (RAM), flash memory, and/or non-volatilestorage. Other hardware, conventional and/or custom, may also beincluded.

Soil moisture prediction processor 208 may include a data selector 226,a data aggregator 228, and a prediction engine 230. In some embodiments,soil moisture prediction processor 208 may execute software to performfunctions associated with each component of soil moisture predictionprocessor 208. In other embodiments, each component of predictionprocessor 208 may be an independent device. In such embodiments, eachcomponent may be hardware configured to specifically process data orperform operations associated with modeling soil moisture levels,generating prediction models and/or handling large data sets. Forexample, data selector 226 may be a field-programmable gate array(FPGA), data aggregator 228 may be a Graphics processing unit (GPU), andprediction engine 230 may be a central processing unit (CPU). Otherhardware combinations are also possible. In yet other embodiments,combinations of hardware and software may be used to implement soilmoisture prediction processor 208.

Data selector 226 may select data from hardware 108′, weather servicedata 146, and/or database 114 based on parameters that indicate the datamay have an effect on a specific property. That is, data selector maydetermine which, of all the data, is indicative of a triggering event.The system itself may learn over time which data is indicative oftriggering events. For example, if the system determines that there is aprecipitation event at a second property (either by hardware 108′located at the second property, weather service data 146, etc.), it maydelay a watering event at the user's property and monitor the soilmoisture levels at the user's property. Over time, the system may learnthe causation associated between the soil moisture levels at the user'sproperty and the triggering events.

Data selector 226 may select a subset of data that are associated with atriggering event by filtering the data set. The data set may be filteredbased on location, for example. Data from locations within a certainradius of the user's property may be used. The radius may be very largedepending on the location of user's property, and data from remotelocations may also be taken based on weather patterns without regard forthe distance from the remote location to the user's property. The datamay be further filtered by a predetermined threshold amount. Forexample, a very small change in soil moisture at a remote property maybe immediately filtered out as a non-triggering event. In someembodiments, data selector 226 may employ techniques, as not limitingexamples, such as the pigeonhole principle, hierarchical verification,and the PEX algorithm, to filter and select data received.

In some embodiments, soil moisture prediction processor 208 mayimplement data selector 226 by executing instructions to create anenvironment in which historical weather data are selected. In otherembodiments, however, data selector 226 may be a separate device orgroup of devices. In such embodiments, data selector 226 may includehardware configured to carry out filtering tasks. For example, toimprove performance and minimize costs, data selector 226 may be anSRAM-based FPGA that functions as a data selector. Data selector 226 mayhave an architecture designed for implementation of specific algorithms.For example, data selector 226 may include a Simple Risc Computer (SRC)architecture or other reconfigurable computing system.

Data aggregator 228 may create a soil moisture function to mapspredictive soil moisture to the data received. The soil moisturefunction may represent an aggregate function of ground truth soilmoisture level at the property for historical triggering eventsdetermined most similar to the current triggering event.

Soil moisture prediction processor 208 may implement data aggregator 228by executing software to create an environment for aggregation of soilmoisture functions representative of historical triggering events.However, in other embodiments data aggregator 228 may includeindependent hardware with specific architectures to improve theefficiency of aggregation or sorting processes. For example, dataaggregator 228 may be a GPU array configured to sort historicaltriggering events and their associated functions and metadata. In someembodiments, data aggregator 228 may be configured to implement sortingalgorithms such as, for example, radix sort. Alternatively oradditionally, data aggregator 228 may be configured to implement aprogramming interface, such as Apache Spark, and execute datastructures, cluster managers, and/or distributed storage systems. Forexample, data aggregator 228 may include a resilient distributed datasetthat is manipulated with a standalone software framework and/or adistributed file system.

Prediction engine 230 may calculate a prediction indication based on oneor more models and a selected data set. For example, prediction engine230 may use a model from model generator 304 and apply inputs based on atriggering event to generate a prediction value for soil moisture levelsfor a specific property. Prediction engine may use a model from modelgenerator 304 and determine a soil moisture function that mapspredictive soil moisture to the triggering event data.

Prediction engine 230 may be implemented by soil moisture predictionprocessor 208. For example, prediction processor 208 may executesoftware to create an environment to execute models. However, in otherembodiments prediction engine 230 may include hardware configured tocarry out parallel operations. Some hardware configurations improve theefficiency of calculations, particularly when multiple calculations arebeing processed in parallel. For example, prediction engine 230 mayinclude multicore processors or computer clusters to divide tasks andquickly perform calculations. In some embodiments, prediction engine mayreceive a plurality of models from model generator 304. In suchembodiments, prediction engine 230 may include a scheduling module. Thescheduling module may receive models and assign each model toindependent processors or cores. In other embodiments, prediction engine230 may be FPGA Arrays to provide greater performance and determinism.

The components of prediction system 204 may be implemented in hardware,software, or a combination of both, as will be apparent to those skilledin the art. For example, although one or more components of predictionsystem 204 may be implemented as computer processing instructionsembodied in computer software, all or a portion of the functionality ofprediction system 204 may be implemented in dedicated hardware. Forinstance, groups of GPUs and/or FPGAs may be used to quickly analyzedata in prediction processor 208.

FIG. 3 is a block diagram of an exemplary model generator, in accordancewith disclosed embodiments. Model generator 304 may include trainingdata module 330, a model processor 308, a model memory 350, and acommunication device 314.

One of the fundamental challenges in obtaining accurate soil moisturepredictions based on triggering events such as precipitation received ata remote location, or a watering event at a remote location, is the lackof sufficient training data sets. Training sets are sets of data used todiscover potentially predictive relationships. They may include an inputvector and an answer vector, that are used together to train an AImachine or a knowledge database. A training data set may only besufficient when it includes a number of samples that enables anartificial intelligence machine or a regression model to be “trained”(e.g., initialized) with an acceptable confidence interval. Sufficienttraining data sets may include samples that represent the fulldistribution to be modeled and have enough samples to identify outliersand minimize deviations. In some embodiments, training data sets includea validation subset and a test subset. In such embodiments, each one ofthe subsets must include a representative sample size.

In some embodiments, training data sets associate triggering events,such as a measured precipitation at a remote location, with ground truthsoil moisture levels within a predetermined amount of time of thetriggering event, that is, actual soil moisture levels measured at aspecific property and obtained directly or indirectly from the moisturelevel sensor at the property within the predetermined amount of time. Inother configurations, training data sets associate soil moisture dataand/or water flow data from a second property with ground truth soilmoisture levels, that is, actual soil moisture levels measured at thespecific first property and obtained directly or indirectly from themoisture level sensor at the first property within a predeterminedamount of time. FIG. 4 shows a representative extended network ofirrigation controllers at three different properties. The irrigationcontrollers may be connected to each other via a network 106, oralternatively, individual components of the irrigation system may beconnected via a network, such as LoRa, Zigbee, etc. For example, soilmoisture sensors 110 at Property 1 may be able to communicate datarelating to their measurements directly to a soil moisture sensor atProperty 3 in a relay-type network that may allow extended communicationeven in the absence of wireless or other available networks.

Training data sets may include information from multiple properties thatare connected together in a larger irrigation system. The system may actas a central source for ground truth of the soil moisture levels foreach specific property. Even more, the system may act as a centralsource for ground truth of the soil moisture levels for each specificmoisture sensor on each property, as multiple moisture sensors on eachproperty each may have a unique moisture level, particularly where theproperty is large and has varying landscape features. In someembodiments model generator 304 may include a training data module 330that monitors network 106 to capture ground truth and facilitategenerating the training data set. Training data module 330 may combineand select information to generate the training data sets used togenerate prediction models.

Training data module 330 includes an event detector 310 and ground truthanalyzer 320, and training data filter 344. Event detector 310 may be asoftware or hardware module configured to detect events in network 106that may be relevant to model generator 304. In some embodiments, eventdetector 310 may be configured to detect when a specific property waters(such as by detecting water flow at the specific property with a waterflow meter). In such embodiments, event detector 310 may signal modelgenerator 304 to request the water flow measurement at the secondproperty, as well as the ground truth soil moisture level at the firstproperty after a predetermined amount of time, and build the trainingdata set.

In addition, event detector 310 may be configured to detect changes insoil moisture at a specific property. For example, if a remote propertywithin a 5 mile radius of the client's property has the soil moistureincrease without a measurement of water flow, event detector 310 mayrequest the soil moisture increase at the remote property, theassociated ground truth soil moisture level at the first property aftera predetermined amount of time, and determine a soil moisture functionthat maps the ground truth soil moisture level to the triggering eventat the remote property, and build the training data set. Event detector310 may create a system to automatically update a training data set ofsoil moisture levels. The ground truth soil moisture may also becompared to weather data to determine if change in soil moisture levelscan be attributed to a watering event, weather precipitation, etc. Thedata training set may also be programmed to include, or in someconfigurations may automatically include, a predetermined acceptablethreshold level for triggering events. For example, a very smallincrease in soil moisture at a remote property may be below thepredetermined acceptable threshold level and thus may not qualify as atriggering event for the user's property. The predetermined acceptablethreshold level may be different for different properties based on soiltypes.

Ground truth analyzer 320 may include software or hardware modulesconfigured to collect and organize data that is associated with aspecific property. Ground truth analyzer 320 may also collect andorganize data associated with other properties within a larger network.

Training data module 330 may include software or hardware modulesconfigured to create a training data set. Training data module 330 maycreate the training data set by combining data for triggering eventsfrom a group of a specific properties and ground truth information forthe same group of a specific properties. For example, communicationdevice 314 may receive a collection of data associated with a triggeringevent generated by a group of a specific properties. If ground truthanalyzer 320 has identified soil moisture levels after a predeterminedamount of time after the triggering event about the same group of aspecific properties, training data module 330 may combine these datasets to create a training data set that includes triggering event dataand ground truth for a group of a specific properties. This may beextended over a broad network.

Training data filter 344 may have a similar configuration to triggeringevent data selector 226 but instead of filtering data associated thatmay indicate a triggering event for a specific property, training datafilter 344 may select a training data subset before it is used togenerate models. The accuracy of the model used to predict soil moisturelevels may vary significantly when different data sets are used. Forexample, predicting soil moisture levels of the client's property inJanuary using a training data set that is based on models for July mayundermine the prediction's accuracy. For this reason, training datafilter 344 may be configured to select training data sets based on, forexample, the type of triggering event. Then, the training data set maybe constrained to certain types of historical triggering events ormodels.

Model processor 308 may include a processor similar to predictionprocessor 208. Model processor may include a model builder andoptionally an accuracy estimator.

Model builder 346 may be software or hardware configured to createprediction models based on the training data. Such prediction models mayinclude functions that map the measured ground truth soil moisturelevels to the triggering event data. In some embodiments, model builder346 may generate decision trees. For example, model builder 346 may taketraining data to generate nodes, splits, and branches in a decisiontree. Model builder 346 may calculate coefficients and hyper parametersof a decision tree based on the training data set. In other embodiments,model builder 346 may use Bayesian algorithms or clustering algorithmsto generate predicting models. In yet other embodiments, model builder346 may use association rule mining, artificial neural networks, and/ordeep learning algorithms to develop models. In some embodiments, toimprove the efficiency of the model generation, model builder 346 may behardware configured to generate models for soil moisture levels. Forexample, model builder 346 may be an FPGA.

Accuracy estimator 348 may be software or hardware configured toevaluate the accuracy of a model. For example, accuracy estimator mayestimate the accuracy of a model, generated by model builder 346, byusing a validation dataset. In some embodiments, the validation data setis a portion of the training data set that was not used to generate theprediction model. Accuracy estimator may generate error rates for eachone of the prediction models. Accuracy estimator 348 may additionallyassign weight coefficients to models based on the estimated accuracy. Insome configurations, an accuracy estimator is not used, as the systemmay track ground truth soil moisture and use it determine accuracy ofthe model in real-time. Additionally, if the model is not accurate, thesystem may take corrective steps as a result For example, if the systemhad delayed a watering event due to a predicted increase in soilmoisture levels, and the soil moisture level did not increase, thesystem could proceed with the delayed watering event. Model memory 350may include model programs 352 and model data 354.

FIG. 4 is a block diagram of one embodiments of a client device 104, inaccordance with disclosed embodiments. In one embodiment, client devices104 may include one or more processors 402, one or more input/output(I/O) devices 404, and one or more memories 410. In some embodiments,client devices 105 may take the form of mobile computing devices such assmartphones or tablets, general purpose computers, or any combination ofthese components. Alternatively, client devices 104 (or systemsincluding client devices 104) may be configured as a particularapparatus, embedded system, dedicated circuit, and the like based on thestorage, execution, and/or implementation of the software instructionsthat perform one or more operations consistent with the disclosedembodiments. According to some embodiments, client devices 104 maycomprise web browsers or similar computing devices that access a website, for example with web-based software, consistent with disclosedembodiments. In one implementation the system 100 is connected to one ormore client devices 104-1, 104-2, 104-3, etc., individually and commonlyreferred to as client device(s) 104 hereinafter, through a communicationnetwork 106. The client devices 104 may provide specific wateringinstructions, and may also be used to inform and/or alert the clientabout watering steps taken by the irrigation system and the state of theirrigation system. The client devices 104 may serve both to providespecific instructions regarding watering, and to provide the client withthe real-time status of the system and the watering actions the systemintends to make based on the prediction system. Such client devices 104include, but are not limited to, desktop computers, hand-held devices,laptops or other portable computers, tablet computers, mobile phones,PDAs, Smartphones, Smart energy meters, Smart home monitoring systems,smart electric appliances, and the like. Further, the client devices 104may include devices capable of exchanging data to provide connectivityto different communicating devices and computing systems. Such devicesmay include, but are not limited to, data cards, mobile adapters,wireless (WiFi™) adapters, routers, a wireless modem, a wirelesscommunication device, a cordless phone, a wireless local loop (WLL)station, and the like. As client devices 104 may be stationary or mobileand may also be understood to be a mobile station, a terminal, an accessterminal, a subscriber unit, a station, etc.

Processor 402 may include one or more known processing devices, such asmobile device microprocessors manufactured by Intel™, NVIDIA™, orvarious processors from other manufacturers. The disclosed embodimentsare not limited to any specific type of processor configured in clientdevices 104. Memory 410 may include one or more storage devicesconfigured to store instructions used by processor 402 to performfunctions related to disclosed embodiments. For example, memory 410 maybe configured with one or more software instructions, such as programs412 that may perform one or more operations when executed by processor402. The disclosed embodiments are not limited to separate programs orcomputers configured to perform dedicated tasks. For example, memory 410may include a single program 412 that performs the functions of theclient devices 104, or program 412 may comprise multiple programs.Memory 410 may also store data 416 that is used by one or more programs412, and/or an irrigation controller application 414.

I/O devices 404 may include one or more devices configured to allow datato be received and/or transmitted by client devices 104 and to allowclient devices 104 to communicate with other machines and devices, suchas other components of system 100. For example, I/O devices 404 mayinclude a screen for displaying optical payment methods such as QuickResponse Codes (QR), or providing information to the user. I/O devices404 may also include components for NFC communication. I/O devices 404may also include one or more digital and/or analog devices that allow auser to interact with client devices 104 such as a touch-sensitive area,buttons, or microphones. I/O devices 404 may also include one or moreaccelerometers to detect the orientation and inertia of client devices104. I/O devices 404 may also include other components known in the artfor interacting with prediction system 204. The components of clientdevices 104 may be implemented in hardware, software, or a combinationof both hardware and software, as will be apparent to those skilled inthe art.

FIG. 6 is an exemplary flow chart illustrating a prediction process andsteps taken to adjust an irrigation system watering schedule as a resultof the prediction process according to disclosed embodiments. In someembodiments, prediction process may be executed by prediction system204.

Prediction system 204 may constantly or nearly constantly receive dataindicative of a potential triggering event, including data from weatherservices, hardware at remote properties, etc., in step 605. Dataindicative of a potential triggering event (640 in FIG. 6 ) may includea measured change in soil moisture levels at a remote property (645), ameasured watering event occurring at a remote property (650), a measuredprecipitation at a remote location (655), etc., and may further includedata relating to the time of the triggering event, an air temperature,date of triggering event, etc. For example, triggering event maycomprise data relating to the amount of precipitation, the location ofthe measured precipitation, the time of the event, and the airtemperature at the time of the event. Additionally many more triggeringevents are contemplated including, but not limited to, utility waterusage, evapotranspiration rates and others.

In step 610, prediction system 204 may filter, or select, a portion ofthe data received from the database based on information contained inthe data received. For example, in embodiments where prediction system204 retrieves all weather data for a geographic area, the predictionsystem may filter the weather data received to only data that isrelevant to occurrences within a predetermined radius of the client'sproperty, such as weather data for a 2-, 5-, 10-, 15-, 20-, 50-, 100-,500-mile, or an even larger radius of the client's property. It alsoneed not be an exact radius. For example, for some properties, thesystem may determine that weather data taken from properties west of theuser's property may be more relevant in determining triggering events.

In some embodiments, prediction system 204 may filter weather servicedata to simplify future analysis. For example, prediction system mayrequire that a precipitation event be measured above a specificthreshold, such as above 0.1 inches. For precipitation events that arelower than the threshold, the prediction system may automatically filterthem out. Similarly prediction system 204 may filter water flow dataand/or moisture sensor data retrieved from other properties based on achange in percentage over time and/or a volume of flow. In suchembodiments, data selector 226 may curate and classify weather servicedata, water flow data, and/or moisture sensor data. For example, dataselector 226 may discard water flow data for locations that areconsidered too remote from the property, and/or may discard water flowdata for locations that are below or “downstream” from the property,even if they are close in proximity. Similarly data selector 226 maydisregarding water flow levels and/or precipitation data that are belowa specific level as these may represent activity that is not related tosoil moisture levels at the property. The data points that do notcorrelate with soil moisture levels at the property may be filteredbased on distance from the property, not reaching a threshold level, orother parameters.

In some embodiments prediction system 204 and data selector 226 may usedata-mining algorithms to efficiently curate, parse, and/or filter theweather service data, water flow measurements, and soil moisture levels.For example, prediction system 204 may use algorithms such as C4.5,Support Vector Machines, Apriori algorithm, Expectation-maximizationalgorithm, among others, to handle large data sets of historical weatherdata, real-time weather service data, water flow measurements, and soilmoisture levels.

In step 615, prediction system 204 may determine whether the correlationof a triggering event (such as a change in water flow at a secondproperty, a change in soil moisture levels at a second property, and/ora measurement of precipitation at a remote location from the property)and a ground truth soil moisture level at the property is sufficient tomake an accurate prediction. In some embodiments, prediction system 204may determine a threshold correlation over a period of time. Forexample, prediction system 204 may set a threshold correlation value. Ifthe threshold is not met, then prediction system 204 may determine thatthe correlation value is not met, and the watering schedule at theproperty may not be changed based on the event. In yet otherembodiments, prediction system 204 may determine the correlation is notsufficient if a requested prediction accuracy cannot be met. Forexample, the system may require a minimum prediction accuracy of 50%. Ifthe available data cannot allow a correlation greater than the requiredpredicted accuracy, the watering schedule at the property may not bechanged as a result of the event. In other configurations, the requiredaccuracy may be higher. However, the system need not require a perfectlyaccurate prediction to proceed, because the system may be able toconstantly or nearly constantly check the accuracy of the predictionbased on ground truth soil moisture sensors. Where the system determinesthe model was not accurate, the system may take corrective action, suchas by performing a baseline watering event that was previously delayedbased on the model. This “wait-and-see” approach to the predictive modelmay serve to save water, as the system may delay scheduled wateringevents by a short period of time even if there is only a 10% chance asoil moisture level may increase.

Correlation of the triggering event may be determined by comparing thepresent triggering event (including data associated with the presenttriggering event, such as the date, time of day, location, andvolume/amount of triggering event) to historical stored triggeringevents with their associated data. In other configurations, there is noneed for a minimum accuracy for the prediction value as the system canmonitor the accuracy of the prediction in real-time based on groundtruth soil moisture levels and adjust as needed.

In step 620, prediction system 204 may generate a function that maps thetriggering event over time in relation to the ground truth soil moisturelevel at the property over time. For example, as the measuredprecipitation at a remote location increases over time, a delayedresponse may be seen in the soil moisture level at the property overtime. The delay in the response may be a direct function of the distancefrom the remote location to the property, and also may be a function ofthe type of triggering event. Predicting a soil moisture increase at theclient's property in response to a precipitation event at a remotelocation may allow the irrigation system at the client's property toeffectively anticipate precipitation that will occur in the near future,and cancel a scheduled watering system in advance of the anticipatedprecipitation.

Similarly, prediction system 204 may generate a function that maps atriggering event such as a measured change in water flow at a secondproperty, over time in relation to the real-time ground truth soilmoisture at the property. For example, watering at the second property(as measured by the change in water flow) may predictably yield anincrease in soil moisture levels at the client's property. This may beparticularly true for adjacent properties and/or a client's propertythat is downstream or otherwise below the second property.

Step 620 may be carried out by prediction engine 230. Prediction enginemay compute a probability of a specific property having a moisture levelincrease based on a triggering event. For example, in step 620prediction system 204 may request from model generator 304 one or moremodels to predict soil moisture levels for a selected a specificproperty. Prediction system 204 may then send prediction models and datato prediction engine 230 to perform the probability calculations.

In some embodiments, prediction engine 230 may receive a plurality offunctions from model generator 304 and compute probabilitiesindependently for each one of the functions. Prediction engine 230 maythen tally each one of the prediction model results. In suchembodiments, prediction engine 230 may determine an accuracy value foreach model and each model result. For example, based on the evaluationperformed by accuracy estimator 348, prediction engine 230 may determinean accuracy value for each model and each model result. Then predictionengine 230 may assign weighting coefficients to the models based on theaccuracy values. In such embodiments, prediction engine 230 may modifythe computed probability for each one of the models (i.e. the modelresults) using the assigned weighting.

In step 625, prediction system 204 may communicate the results list tothe irrigation controller, and the irrigation controller may take actionautomatically based on the predicted soil moisture level, by revising ascheduled baseline watering event. For example, if the irrigationcontroller was going to begin watering a property within the next 30minutes, but the prediction system 204 predicts the soil moisture levelwill increase on the property in the next 60 minutes based on thetriggering event at the remote location, the irrigation controller mayautomatically alter, delay or cancel the scheduled watering.

In step 630, the system may monitor ground truth soil moisture levels todetermine the accuracy of the predictive soil moisture function. In thismanner, the system may be constantly building the training data set,including for real-time events that the system made a prediction for.Additionally, by monitoring the ground truth soil moisture level, thesystem may make further adjustments, if needed. For example, if the soilmoisture level at the client's property did not increase as predicted bythe predictive soil moisture function, the AI may instruct theirrigation system to perform the previously delayed baseline wateringevent or alternatively adjust the scheduled watering to a shorterduration. In step 635, the irrigation controller may control the systemhardware, such as sprinklers 112, based on the revised baselineschedule.

Generating a prediction soil moisture function may be carried out byprediction system 204. The prediction system 204 may obtain a model forthe specific triggering event based on a correlation to a storedhistorical triggering event. In some embodiments, prediction system 204may send a request to model generator 304 to generate prediction modelsfor the specific triggering event. Model generator 304 may respond withone or more prediction models for the specific triggering event basedon, for example, the classification of the type of triggering event andthe location of the triggering event. For instance, model generator 304may send to prediction system 204 a group of prediction modelsspecifically for a watering event detected at an adjacent property tothe client's property. Or model generator 304 may send to the predictionsystem 204 a group of prediction models specifically for a precipitationevent detected at a nearby city.

Prediction system 204 may compare the correlation values of eachprediction model, using the metadata associated with the storedhistorical triggering event along with the data relating to the presenttriggering event. Weights may be given to different aspects of themetadata. For example, the location of the triggering event may be givenmore weight than a date of the triggering event. The system maydetermine what features may be given specific weights, and system maychange the weights based on the stored model features. For example,there may be a very strong correlation between the location of aprecipitation event and moisture levels at the client's property, nomatter what date or time of the precipitation. Similarly, there may be astrong correlation between a watering event at an adjacent property andmoisture levels at the client's property, but only below a certain airtemperature. Based on weighted correlation values of the predictionmodels, the system may select a specific prediction model, or the systemmay generate a new prediction model as a weighted average of theprediction models.

FIG. 7 is an exemplary flow chart illustrating a training set generationprocess, in accordance with disclosed embodiments. In some embodiments,model generator 304 may carry out training set process. In step 705,model generator 304 may receive data indicative of a potentialtriggering event, such as soil moisture levels measured over time atproperties in the extended irrigation network, water flow measurementsover time at properties in the extended irrigation network, and/orweather service data. Data indicative of a potential triggering event(640 in FIG. 6 ) may include a measured change in soil moisture levelsat a remote property (745), a measured watering event occurring at aremote property (750), a measured precipitation at a remote locationsuch as from weather service data (755), etc.

In step 710, model generator 304 may filter data to determine if atriggering event has occurred, such as by filtering data as describedabove with respect to FIG. 7 . In step 715, model generator 304 mayreceive and plot the ground truth soil moisture levels over apredetermined time period following the triggering event. For example,15 minutes, 30 minutes, 60 minutes, 90 minutes, 120 minutes, etc.

In step 720, model generator 304 may determine a soil moisture functionthat maps ground truth soil moisture levels to the triggering eventdata. For example, for a precipitation event detected in a city 5 milesaway, the model generator may determine that soil moisture increasesafter a delay or lag time of 10 minutes and generate a soil moisturefunction to represent the lag time for the ground truth soil moisturelevel in response to the triggering event. The soil moisture functiondetermined by model generator may also account for the amount ofprecipitation, for example, the soil moisture function may account forlower ground truth soil moisture levels for a precipitation of 0.2inches and higher ground truth soil moisture levels for a precipitationof 0.5 inches.

In step 725, model generator 304 may build the training data set bystoring the soil moisture function along with model features, such asmetadata associated with the triggering event. Model features mayrepresent a variable useful for prediction that may be used in theprediction solution. For example, model features may include the amountof precipitation received within a period, a location associated withthe triggering event, a date, a time of day, an air temperature, or aspecific property type (such as a property that is uphill/upstream fromthe client property). In certain embodiments, model generator 304 maystore multiple model features and determine a feature importance scorefor all the stored model features. The feature importance score mayreflect the correlation between the selected features and otheridentified features. For example, model generator 304 may assign a highimportance score to a variable or feature that exhibits a highcorrelation with the other identified features. Alternatively, a featurewith low correlation with respect to other features may be assigned alow importance score. Furthermore, in additional or alternativeembodiments, the model features may be categorized based in theinfluence they have in the target result. In some embodiments, the inputmodel features may then be used to determine a model. For example, in adecision tree input model features may be used to make nodedeterminations.

In some embodiments, model generator 304 may score model features usingtechniques such as Principal Component analysis and/or unsupervisedclustering methods. In other embodiments model generator 304 may includeline or edge detection, or Digital Signal Processing (DSP) methods. Insuch embodiments, model generator 304 may implement iterativeregressions to evaluate features. For example, model generator 304 mayexecute algorithms such as least absolute shrinkage and selectionoperator to determine least squares models for each generated feature toestimate a feature behavior. It will be appreciated that a combinationof each of the analyses may be utilized as well.

In some embodiments, training data module 330 may select data based on aspecific triggering event detected. For example, prediction system 204may request a model from model generator 304 for a watering eventdetected at an adjacent property. Training data module 330 may generatea training data set only using soil moisture functions and ground truthfor watering events at the specific adjacent property.

Model generator 304 may generate a candidate model using a training dataset. For example, model generator may process the training data set ofstep 705 to determine coefficients and hyper parameters for a predictionmodel. The prediction models may be parametric, non-parametric, orsemi-parametric. In some embodiments, model generator 304 may create aplurality of decision trees as prediction models. For example, modelgenerator 304 may use a top-down or a bottom-up approach to generatecandidate decision trees in step 720. In such embodiments, modelgenerator 304 may generate nodes by finding a discrete function of theinput attributes values using a training data set or a training datasubset. Based on a splitting metric, a coefficient may be assigned tothe node. Decision trees created by model generator 304 may then be usedin a random forest analysis.

In other embodiments, model generator 304 may generate neural networks,Group Method of Data Handling (GMDH) algorithms, Naive Bayesclassifiers, and/or Multivariate Adaptive Regression Splines. Forexample, model generator 304 may implement Convolutional Neural Networks(CNN), generating nodes and connections associated with multipledimensions using the training data. CNNs consist of multiple layers ofreceptive fields and various combinations of convolutional and fullyconnected layers. Additionally, model generator 304 may implementRecurrent Neural Networks (RNN), generating nodes and connections withdirected cycles that dynamically adjust to behaviors of the trainingdata.

As indicated at step 730, the process may be repeated a plurality oftimes to generate a plurality of models. In some embodiments, modelgenerator 304 may repeat the process until a minimum of models isgenerated. For example, prediction system 204 may request that modelgenerator 304 generates models for a random forest analysis. In suchembodiments, model generator 304 may generate between 20-100 decisiontrees to conduct the prediction analysis. Less than 20 models may notprovide the required accuracy while more than a 100 decision trees maydemand too much computing power. In such embodiments, prediction systemmay require at least 50 decision trees.

FIG. 8A is an exemplary predictive soil moisture function, in accordancewith disclosed embodiments. In this example, over time, precipitation ata remote location affects the soil moisture levels at the client'sproperty. Here, the precipitation event at the remote location has begunat time value 0. The precipitation event continues at the remotelocation and the rainfall level increases over time. After a lag time of5 minutes, the soil moisture levels at the client's property begin toincrease. The system could store this as a training function mappingground truth soil moisture of the client's property as a function of theprecipitation triggering event at the remote property. Along with thetraining function, associated metadata such as the location of theremote property, the time of day of the triggering event, thevolume/amount of precipitation of the triggering event, a type for thetriggering event (in this case, “precipitation,” other types may beevents such as a watering event, etc.), a date of the triggering event,etc. may be stored. The system may later retrieve these functions basedon the associated metadata to determine the best predictive function forsoil moisture of the client's property as a function of the triggeringevent.

While FIG. 8A contemplates a lag time of only 5 minutes, it will beappreciated that the lag time could be much longer depending on thelocation of the remote property associated with the triggering event.For example, the lag time could be a few hours or even a few days. FIG.8B shows another exemplary predictive soil moisture function, inaccordance with disclosed embodiments. Like in FIG. 8A, FIG. 8B showshow over time, precipitation at a remote location affects the soilmoisture levels at the client's property. Here, the precipitation eventat the remote location has begun at time value 0. The precipitationevent continues at the remote location and the rainfall level increasesover time. After a lag time of 5 hours, the soil moisture levels at theclient's property begin to increase. The system could store this as atraining function mapping ground truth soil moisture of the client'sproperty as a function of the precipitation triggering event at theremote property. Along with the training function, associated metadatacould be stored.

FIG. 8C is another exemplary predictive soil moisture function, inaccordance with disclosed embodiments. In this example, over time, soilmoisture levels at a remote location affects the soil moisture levels atthe client's property. For example, the remote property could be aproperty adjacent to the client's property or uphill/upstream from theclient's property. Here, the triggering event at the remote location hasbegun at time value 0. For example, watering at the remote property maybegin at time value 0. The watering event continues at the remoteproperty and the soil moisture level increases over time. After a lagtime of 5 minutes, the soil moisture levels at the client's propertybegin to increase. The system could store this as a training functionmapping ground truth soil moisture of the client's property as afunction of the triggering event at the remote property. Along with thetraining function, associated metadata may be stored.

FIG. 9 is an exemplary flow chart illustrating an overall process thatmay be taken by an irrigation controller. The irrigation controller mayfirst establish a baseline watering schedule at step 905. This may bedone, for example, by analysis of historical watering patterns.Historical weather data from years past that is maintained in weathersystems and even local archives may be obtained and utilized todetermine the baseline watering schedule. Historical data or other datastored within the system's memory or on database 114 may provide thisdata to the AI feature. For example a new program water schedule createdfor spring annual flowers may have been implemented from previous yearswithin specific dates and for specific irrigation amounts and times. TheAI feature may recognize from historical data that this was done andwould suggest or create a baseline watering schedule that takes intoaccount the spring annual flowers historical watering schedule createdin prior years. This may also be applied to other schedule or serviceappointment such as lawn mowing schedules, and the AI may review prioryear's data and make suggestions or auto create appointments orschedules that impact the watering program.

After establishing the baseline watering schedule, the controller mayquery to determine if the prediction system has detected a triggeringevent at step 910. For example, the prediction system 204 may gatherreal-time data relating to onsite rain fall or moisture content in theground or atmosphere or other environmental features. With thisinformation regarding moisture content or rainfall the system maydetermine a predictive soil moisture function at step 915, and inresponse may automatically increase, decrease or otherwise modify thebaseline watering schedule and control the irrigation system in responseat step 920. This may be done based on the needs of surroundingproperties or based on the same inputs from the current property thesystem is monitoring. One example may include a system that may not haverain sensors, moisture sensors or environmental sensors, while a nearbythat property might have any number of these. The AI feature may relayor pull from other property's specific data to make adjustments to thebaseline watering schedule at the client's property. This may also allowirrigation customers who may not have all the features of surroundingproperties to still take advantage of surrounding property data. Forexample, as seen in the extended network of irrigation controllers inFIG. 4 , property 2 does not have dedicated soil moisture sensors norwater flow detectors. Property 3 and/or property 1 may provide groundtruth data for property 2.

Although the description herein is explained with reference to a clientcommunicating device such as a smart device, the described methods mayalso be implemented in any other devices, as will be understood by thoseskilled in the art. For a firmware, and/or software implementation, themethodologies can be implemented with modules (e.g., procedures,functions, and so on) that perform the functions described herein. Anymachine readable medium tangibly embodying instructions can be used inimplementing the methodologies described herein. For example, softwarecodes and programs can be stored in a memory and executed by aprocessing unit. Memory can be implemented within the processing unit ormay be external to the processing unit. As used herein the term “memory”refers to any type of long term, short term, volatile, nonvolatile, orother storage devices and is not to be limited to any particular type ofmemory or number of memories, or type of media upon which memory isstored.

The irrigation system 100 described herein (FIG. 1 ), can be implementedin any network environment comprising a variety of network devices,including routers, bridges, servers, computing devices, storage devices,etc. The communication network, and the various devices therein, maycontain other devices, systems and/or media not specifically describedabove. For example, in one implementation it is anticipated that thesystem 100 may be implemented on an application server or web server. Insuch a configuration, a client device 104 may communicate instructionsto an application or web server, and/or receive input from anapplication or web server. The application or web server may communicateinstructions to hardware 108, such as a sprinklers 112 to control suchhardware in response to the predicted soil moisture level from themoisture content prediction system and communicated to the hardware 108(described in additional detail below with reference to FIG. 3 ). Inanother configuration, the system 100 may include not just anapplication or web server, but the system may also comprise hardware tobe controlled by the system, such as one or more watering devices, etc.(see block diagram of system 100 in FIG. 2 ). It will be appreciatedthat various aspects of the system may be implemented either byincluding the hardware to be controlled within the system, or bycommunication of a signal to the hardware to be controlled outside thesystem by a network 106.

A system for predicting soil moisture at a first property based on atriggering event, and managing an irrigation system at the firstproperty in response to the triggering event the system may comprise: aprocessor in communication with a prediction system and a database; anda storage medium storing instructions that, when executed, configure theprocessor to: establish a baseline watering schedule comprising one ormore baseline watering events; receive at least one of real-time weatherdata, real-time water flow data, and real-time soil moisture data;filter the at least one of weather data, water flow data, and soilmoisture data to determine if a potential triggering event has occurred;where a potential triggering event has occurred, comparing at least onefeature of the potential triggering event to at least one model featureof stored historical triggering event models to determine a correlationvalue between the potential triggering event and stored historicaltriggering events, the model features including at least one of type,location, date, time, and amount; where the correlation value is above apredetermined threshold, making a soil moisture prediction for the firstproperty based on at least one stored historical triggering event model;and where soil moisture prediction for the first property will increasewithin a predetermined amount of time of a baseline watering event,delaying the baseline watering event.

In some configurations, the soil moisture function comprises a storedsoil moisture function mapping ground truth soil moisture data to thestored historical triggering event. The system may further comprise thestorage medium storing instructions that, when executed, configure theprocessor to build a training set, the training set comprising soilmoisture functions for the first property that map predictive soilmoisture at the first property to at least one of real-time weatherdata, real-time water flow data, and real-time soil moisture data takenat a location remote from the property.

The instructions that configure the processor to build the training setmay comprise instructions that configure the processor to: receive atleast one of weather data, water flow data, and soil moisture data takenat a location remote from the first property; filter the at least one ofweather data, water flow data, and soil moisture data to determine if apotential triggering event has occurred; receive a ground truth soilmoisture level at the property over a predetermined time periodfollowing the potential triggering event; determine a soil moisturefunction that maps predictive soil moisture to the at least one ofweather data, water flow data, and soil moisture data over thepredetermined time period following the potential triggering event; whenthe soil moisture function maps an increase in predictive soil moisture,tag the potential triggering event as a historical triggering event; andstore the historical triggering event and the soil moisture functionwith its metadata, the metadata comprising at least one of: a type, alocation, a lag time, and an amount.

A model feature of stored historical triggering event models may includeat least one of a type of the triggering event, a location of thetriggering event, and an amount of the triggering event. A type oftriggering event may include at least one of a soil moisture change overtime at the second property, a water flow measurement change over timeat the second property, and weather service data from a location remotefrom the first property.

A feature of the potential triggering event may include at least one ofa type of the potential triggering event, a location of the potentialtriggering event, and an amount of the potential triggering event.

According to another aspect, a system is disclosed for predicting soilmoisture at a first property based on a triggering event, and managingan irrigation system at the first property in response to the triggeringevent, the system comprising: a processor in communication with aprediction system and a database; and a storage medium storinginstructions that, when executed, configure the processor to: establisha baseline watering schedule comprising one or more baseline wateringevents; receive data associated with a potential triggering event at asecond property, comparing at least one characteristic of the potentialtriggering event to at least one characteristic of stored historicaltriggering events to determine a correlation value between the potentialtriggering event and stored historical triggering events, the storedhistorical triggering events comprising metadata including at least oneof location of the stored historical triggering event, and an amount ofthe stored historical triggering event; where the correlation value isabove a predetermined threshold, generating a soil moisture predictionmodel for the first property based on a soil moisture function of atleast one stored historical triggering event; and where soil moistureprediction for the first property will increase within a predeterminedamount of time of a baseline watering event, delaying the baselinewatering event.

The instructions may further configure the processor to: determine asoil moisture level prediction model for the triggering event based onground truth soil moisture levels over a predetermined timeframe;generate a training data set based on the triggering event and soilmoisture level prediction model; and communicate the soil moisture levelprediction model for the triggering event to the prediction system.

A system is also disclosed for predicting soil moisture at a firstproperty based on a precipitation event at a location remote from thefirst property, and managing an irrigation system at the first propertyin response to the precipitation event at the location remote from thefirst property, the system comprising: a processor in communication witha prediction system and a database; and a storage medium storinginstructions that, when executed, configure the processor to: establisha baseline watering schedule comprising one or more baseline wateringevents; receive real-time weather data for the location remote from thefirst property; filter the real-time weather data for the locationremote from the first property to determine if the precipitation eventhas occurred; where the precipitation event has occurred, generating apredictive soil moisture model based on at least one stored historicalprecipitation event models, the at least one stored historicalprecipitation event model comprising model features; and modifying thebaseline watering schedule in response to the predictive soil moisturemodel.

In some configurations, the processor may determine an accuracy of thepredictive soil moisture model based on ground truth soil moisturelevels. The precipitation event may further comprise features, thefeatures comprises at least one of a location of the precipitationevent, a date of the precipitation event, a time of the precipitationevent, and an amount of the precipitation event. Similarly, the modelfeatures may comprise at least one of a location of the historicalprecipitation event, a date of the historical precipitation event, atime of the historical precipitation event, and an amount of thehistorical precipitation event.

Although the foregoing disclosure provides many specifics, these shouldnot be construed as limiting the scope any of the ensuing claims. Otherembodiments may be devised which do not depart from the scopes of theclaims. Features from different embodiments may be employed separatelyor in combination. Accordingly, all additions, deletions andmodifications to the disclosed subject matter that fall within thescopes of the claims are to be embraced thereby. The scope of each claimis indicated and limited only by its plain language and the full scopeof available legal equivalents to its elements.

I claim:
 1. A system for predicting soil moisture at a first propertybased on a triggering event, and managing an irrigation system at thefirst property in response to the triggering event the systemcomprising: a processor in communication with a prediction system and adatabase; and a storage medium storing instructions that, when executed,configure the processor to: establish a baseline watering schedulecomprising one or more baseline watering events; receive at least one ofweather data and water flow data; filter the at least one of weatherdata and water flow data to determine if a potential triggering eventhas occurred; where the potential triggering event has occurred,comparing at least one feature of the potential triggering event to atleast one model feature of stored historical triggering event models todetermine a correlation value between the potential triggering event andstored historical triggering events, the at least one model featureincluding at least one of type, location, date, time, and amount; wherethe correlation value is above a predetermined threshold, making a soilmoisture prediction for the first property based on the at least onestored historical triggering event model; and where soil moistureprediction for the first property will increase within a predeterminedamount of time of a baseline watering event, delaying the baselinewatering event.
 2. The system of claim 1, wherein the soil moistureprediction comprises a stored soil moisture function mapping groundtruth soil moisture data to the stored historical triggering eventmodels.
 3. The system of claim 1, further comprising the storage mediumstoring instructions that, when executed, configure the processor tobuild a training set, the training set comprising soil moisturefunctions for the first property that map predictive soil moisture atthe first property to at least one of weather data and water flow datataken at a location remote from the first property.
 4. The system ofclaim 3, wherein the instructions that configure the processor to buildthe training set comprise instructions that configure the processor to:receive at least one of weather data and water flow data taken at alocation remote from the first property; filter the at least one ofweather data and water flow data to determine if a potential triggeringevent has occurred; receive a ground truth soil moisture level at theproperty over a predetermined time period following the potentialtriggering event; determine a soil moisture function that mapspredictive soil moisture to the at least one of weather data and waterflow data over the predetermined time period following the potentialtriggering event; when the soil moisture function maps an increase inpredictive soil moisture, tag the potential triggering event as ahistorical triggering event; and store the historical triggering eventand the soil moisture function with its metadata, the metadatacomprising at least one of: a type, a location, a lag time, and anamount.
 5. The system of claim 1, wherein at least one model feature ofstored historical triggering event models comprises at least one of atype of the triggering event, a location of the triggering event, and anamount of the triggering event.
 6. The system of claim 5, wherein a typeof triggering event comprises at least one of a soil moisture changeover time at a second property, a water flow measurement change overtime at the second property, and weather service data from a locationremote from the first property.
 7. The system of claim 1, wherein atleast one feature of the potential triggering event comprises at leastone of a type of the potential triggering event, a location of thepotential triggering event, and an amount of the potential triggeringevent.
 8. The system of claim 1, wherein the processor configured tofilter the at least one of weather data and water flow data to determineif a potential triggering event has occurred comprises the processorfiltering the at least one of weather data and water flow data based onat least one of location, type, and amount.
 9. The system of claim 1,wherein the system further comprises at least one soil moisture sensorin communication with the processor, and wherein the processor isfurther configured to receive ground truth soil moisture data from theat least one soil moisture sensor and compare the soil moistureprediction to the ground truth soil moisture data.
 10. The system ofclaim 9, wherein when the processor is further configured to activatethe baseline watering event after a predetermined amount of time if theground truth soil moisture data is lower than the soil moistureprediction.
 11. A system for predicting soil moisture at a firstproperty based on a precipitation event at a location remote from thefirst property, and managing an irrigation system at the first propertyin response to the precipitation event at the location remote from thefirst property, the system comprising: a processor in communication witha prediction system and a database; and a storage medium storinginstructions that, when executed, configure the processor to: establisha baseline watering schedule comprising one or more baseline wateringevents; receive weather data for the location remote from the firstproperty; filter the weather data for the location remote from the firstproperty to determine if the precipitation event has occurred; where theprecipitation event has occurred, generating a predictive soil moisturemodel based on at least one stored historical precipitation event model,the at least one stored historical precipitation event model comprisingmodel features; and modifying the baseline watering schedule in responseto the predictive soil moisture model.
 12. The system of claim 11,wherein the instructions, when executed, further configure the processorto determine an accuracy of the predictive soil moisture model based onground truth soil moisture levels.
 13. The system of claim 12, whereinthe precipitation event further comprises features, the featurescomprises at least one of a location of the precipitation event, a dateof the precipitation event, a time of the precipitation event, and anamount of the precipitation event.
 14. The system of claim 13, whereinthe model features comprise at least one of a location of the storedhistorical precipitation event, a date of the historical precipitationevent, a time of the historical precipitation event, and an amount ofthe historical precipitation event.
 15. A system for predicting soilmoisture at a first property based on a triggering event, and managingan irrigation system at the first property in response to the triggeringevent the system comprising: a processor in communication with aprediction system and a database; and a storage medium storinginstructions that, when executed, configure the processor to: receive atleast one of weather data, rain data, and water flow data; filter the atleast one of the weather data, the rain data, and the water flow data todetermine if a potential triggering event has occurred; where thepotential triggering event has occurred, comparing at least one featureof the potential triggering event to at least one model feature ofstored historical triggering event models to determine a correlationvalue between the potential triggering event and stored historicaltriggering events, the at least one model feature including at least oneof type, location, date, time, and amount; where the correlation valueis above a predetermined threshold, making a soil moisture predictionfor the first property based on the at least one stored historicaltriggering event model; and where soil moisture prediction for the firstproperty will increase within a predetermined amount of time of abaseline watering event, delaying a baseline watering event of apredetermined baseline watering schedule.
 16. The system of claim 15,wherein the soil moisture prediction comprises a stored soil moisturefunction mapping ground truth soil moisture data to the storedhistorical triggering event models.
 17. The system of claim 16, furthercomprising the storage medium storing instructions that, when executed,configure the processor to build a training set, the training setcomprising soil moisture functions for the first property that mappredictive soil moisture at the first property to at least one of theweather data, the water flow data, and the rain data taken at a locationremote from the first property.
 18. The system of claim 17, wherein theinstructions that configure the processor to build the training setcomprise instructions that configure the processor to: receive at leastone of weather data and water flow data taken at a location remote fromthe first property; filter the at least one of weather data and waterflow data to determine if a potential triggering event has occurred;receive a ground truth soil moisture level at the property over apredetermined time period following the potential triggering event;determine a soil moisture function that maps predictive soil moisture tothe at least one of weather data and water flow data over thepredetermined time period following the potential triggering event; whenthe soil moisture function maps an increase in predictive soil moisture,tag the potential triggering event as a historical triggering event; andstore the historical triggering event and the soil moisture functionwith its metadata, the metadata comprising at least one of: a type, alocation, a lag time, and an amount.
 19. The system of claim 15, whereinat least one model feature of stored historical triggering event modelscomprises at least one of a type of the triggering event, a location ofthe triggering event, and an amount of the triggering event.
 20. Thesystem of claim 19, wherein a type of triggering event comprises atleast one of a soil moisture change over time at a second property, awater flow measurement change over time at the second property, andweather service data from a location remote from the first property.